Systems and methods for concurrent summarization of indexed data

ABSTRACT

Provided are systems and methods for concurrent summarization of indexed data. In some embodiments, two or more summary processes can be executed concurrently (e.g., in parallel) by an indexer to generate summaries for respective subsets of indexed data (e.g., partitions or buckets of indexed data) managed by the indexer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/694,797, Attorney Docket SPLK-0016-01.01US, filed Apr. 23, 2015, theentire disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure is generally directed to data processing, andmore particularly, to systems and methods for concurrent summarizationof indexed data.

BACKGROUND

Modern data centers often comprise thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of machine-generated data. The unstructured natureof much of this data has made it challenging to perform indexing andsearching operations because of the difficulty of applying semanticmeaning to unstructured data. As the number of hosts and clientsassociated with a data center continues to grow, processing largevolumes of machine-generated data in an intelligent manner andeffectively presenting the results of such processing continues to be apriority.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are block diagrams that illustrates example summarizationprocesses in accordance with the disclosed embodiments.

FIG. 2 is block diagram that illustrates an example data processingenvironment in accordance with the disclosed embodiments.

FIG. 3A is a flowchart that illustrates an example method for generatingsummary requests in accordance with the disclosed embodiments.

FIG. 3B is a flowchart that illustrates an example method for launchingsummarization processes in accordance with the disclosed embodiments.

FIG. 3C is a flowchart that illustrates an example summarizationprocesses in accordance with the disclosed embodiments.

FIG. 4 is a diagram that illustrates an example computer system inaccordance with one or more embodiments.

FIG. 5 presents a block diagram of an example event-processing system inaccordance with the disclosed embodiments.

FIG. 6 presents a flowchart illustrating an example of how indexersprocess, index, and store data received from forwarders in accordancewith the disclosed embodiments.

FIG. 7 presents a flowchart illustrating an example of how a search headand indexers perform a search query in accordance with the disclosedembodiments.

FIG. 8 presents a block diagram of an example system for processingsearch requests that uses extraction rules for field values inaccordance with the disclosed embodiments.

FIG. 9 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments.

FIG. 10A illustrates an example search screen in accordance with thedisclosed embodiments.

FIG. 10B illustrates an example data summary dialog that enables a userto select various data sources in accordance with the disclosedembodiments.

FIG. 11A illustrates an example key indicators view in accordance withthe disclosed embodiments.

FIG. 11B illustrates an example incident review dashboard in accordancewith the disclosed embodiments.

FIG. 11C illustrates an example proactive monitoring tree in accordancewith the disclosed embodiments.

FIG. 11D illustrates an example screen displaying both log data andperformance data in accordance with the disclosed embodiments.

DETAILED DESCRIPTION

The present disclosure is directed to concurrent summarization ofindexed data. In some embodiments, two or more summary processes can beexecuted concurrently (e.g., in parallel by an indexer) to generatesummaries for respective subsets of indexed data (e.g., summaries ofpartitions or buckets of indexed data managed by the indexer). In someembodiments, a file (or directory) associated with a particular summaryfor a subset of data is locked when a process is writing summary data toit, thereby inhibiting other processes from writing conflicting orredundant summary data to the file (or directory) for the same subset ofdata. In response to determining that a file (or directory) associatedwith a particular summary for a subset of data is locked, a process mayproceed to summarizing another subset of data for which the file (ordirectory) associated with the particular summary for the other subsetof data is not locked. Such embodiments can enable multiplesummarizations processes to execute in parallel to generate similarsummaries for multiple subsets of data (e.g., similar summaries forpartitions or buckets of indexed data) without creating conflicting orredundant summaries for the subsets of data.

As described herein, in some embodiments, an index includes partitionsof indexed machine-generated data (also referred to as “buckets”) thatare managed by an indexer. For example, an indexer may receivemachine-generated data (e.g., log data generated by an e-mail server),the data may be parsed into events, and the indexer may index and storethe events in buckets managed by the indexer. The events may be timestamped (or otherwise associated with a time) and the indexer may indexand store the events in certain buckets based on the time associatedwith the events. For example, the indexer may index and store the first1000 events of the log data generated by the e-mail, server in a firstbucket, index and store the second 1000 events of the log data generatedby the e-mail server in a second bucket, and so forth. Thus, each of thebuckets may store a subset of events of a similar age, or at least asubset of events associated with a given timespan (e.g., a timespan fromthe time associated with the earliest event in the bucket to the timeassociated with latest event in the bucket). Although certainembodiments are describe with regard to an indexers maintaining indexesof machine-data from an e-mail server for the purpose of illustration,an indexer can maintain any number of buckets of indexed machined datareceived from any variety of sources as described herein.

In some embodiments, a summary process can be executed to generate asummary of data in a bucket. For example, an indexer can execute asummary process to generate a summary (also referred to as a “bucketsummary”) that includes information indicating the average size ofe-mails based the data contained in the events stored in a bucket (e.g.,a subset of events generated by an e-mail server). In some embodiments,a summary can include a report summary that can be used, for example, toaccelerate generation of reports concerning the summarized data. Areport summary may include a pre-calculated statistic (“stat”) (alsoreferred to as a “pre-computed aggregate”) that can be used in thegeneration of statistics (or aggregates) of a report on the summarizeddata, or a larger set of data. For example, in the context of eventsgenerated by an e-mail server, a bucket report summary may include acount of events in a bucket that include a value for a size field (e.g.,1000 events that include a “size” field) and a sum of the values for thesize fields in those events (e.g., 1000 MB (megabytes) total, for thesize values in the 1000 events). Such pre-computed values can be used,for example, to generate an average “size” e-mail for that bucket ofdata (e.g., an average e-mail size of 1 MB for the bucket). In someembodiments, a more complete report can be generated across a larger setof data, such as a large number of buckets managed by one or moreindexers, using these types of report summaries. For example, an entity,such as a search head, may request these types of bucket reportsummaries from all of the indexers in a system, and combine the countsof events and the size values from all of the bucket report summaries togenerate an average e-mail size indicated by the larger set of data(e.g., the average e-mail size from the events indicated by the eventsstored the buckets of the indexers). This process may be referred to asan accelerated reporting. With accelerated reporting, the speed ofgenerating the overall report at the search head can be increased due tothe fact that pre-generated bucket report summaries (e.g., bucket reportsummaries stored at the indexer) can be retrieved and used to generatethe reports. Thus, generation of the overall report summary at thesearch head can avoid some or all of the processing overhead and timeassociated with generating the bucket level report summaries becausethat processing has already been completed by the indexers. Reportacceleration and associated embodiments are described in more detailherein with regard to at least FIGS. 5-11D.

In some embodiments, a summary can include a data model summary that canbe used, for example, to accelerate generation of generating reports onattributes (also referred to as “fields”) of a data model. As describedherein, a data model can include one or more “objects” (also referred,to as “data model objects”) that define or otherwise correspond to aspecific set of data. A data model object may be defined by: (1) a setof search constraints; and (2) a set of fields. Thus, a data modelobject can be used to quickly search data to identify a set of events(e.g., a set of events that satisfy the set of search constraints of thedata model object) and a set of fields associated with the set of events(e.g., the set of fields of the data model object that are in the set ofevents identified). For example, an “e-mails processed” data modelobject may specify a search for events relating to e-mails that havebeen processed by a given e-mail server, and specify a set of fields(e.g., date, size, etc.) that are associated with the events. Thus, auser can retrieve and use the “e-mails processed” data model object toquickly retrieve a listing of the set of fields (e.g., date, size, etc.)of the events relating to e-mails processed by the given e-mail server.By using a data model, the user may not have to recreate the search andre-identify the fields of interest. Example embodiments of data modelsand example usage of data models is described in U.S. patent applicationSer. No. 14/503,335 titled “Generating Reports from Unstructured Data”and filed on Sep. 30, 2014, which is hereby incorporated by reference inits entirety.

A data model summary for a data model can include or otherwise identifyvalues the fields (or attributes) specified by the data model that arefrom the events that satisfy the search criteria of the data model. Forexample, if a data model objects specifies (1) search criteria of eventsfrom e-mail servers, and (2) attribute of “size”, and a bucket includes10,000 events total, with 1,000 of the 10,000 events having beengenerated by an e-mail server and including a field for size, then thedata model summary may include or otherwise identify the 1,000 eventsand/or the values of the “size” fields of the 1,000 events. Thus, theset of data associated with the data model summary may be significantlysmaller in size than the original set of data that is summarized. Insome embodiments, a more complete report can be generated across alarger set of data, such as a large number of buckets managed by one ormore indexers, using these types of data model summaries. For example, asearch head may request these types of bucket data model summaries fromall of the indexes in a system, and combine the events and/or values forthe fields in all of the bucket data model summaries to generate fullset of events and/or values for the fields of the data model. Thisprocess may be referred to as data model acceleration. With data modelacceleration, the speed of generating the full set of data (e.g., valuesfor the fields of the data model) at the search head can be increaseddue to the fact that the pre-generated data model summaries for thebuckets (e.g., bucket data model summaries stored at the indexer) can beused to drive the features that make use of the data model (e.g., topopulate the values for the fields of a pivot visualization generatedusing the data model). Thus, generation of the overall data model at thesearch head can avoid some or all of the processing overhead and timeassociated with generating the bucket level data model summaries becausethat processing has already been completed by the indexers.

The embodiments described herein can be employed with various techniquesof report and/or data model acceleration, including those described inU.S. Pat. No. 8,682,925 titled “Distributed High Performance AnalyticsData Store” issued Mar. 25, 2014; U.S. Pat. No. 8,682,886 titled “ReportAcceleration Using Intermediate Summaries of Events” issued Mar. 25,2014; and U.S. Pat. No. 8,516,008 titled “Flexible Schema Column Store”issued Aug. 20, 2013, which are each hereby incorporated by reference intheir entireties.

In some embodiments, two or more summary processes can be executedconcurrently (e.g., in parallel). Continuing with the above example, theindexer may execute a first summary process on the first bucket tosummarize the 1000 events of e-mail server log data in the first bucket,and concurrently execute a second summary process on the second bucketto summarize the 1000 events of server log data in the second bucket. Insome embodiments, each process may be executed by a different thread ofone or more processors of the indexer. For example, the first processmay be executed by a first thread of a processor of the indexer, and thesecond process may be executed by a second thread of the processor ofthe indexer.

In some embodiments, the summary processes can generate the samesummaries for each of the buckets such that the processes concurrentlyto generate the same summaries for the data in the respective buckets.For example, a first summary process executed by the indexer can includea report summary for the average e-mail size indicated by events storedin the first bucket, a second summary process executed by the indexercan include a report summary for the average e-mail size indicated byevents stored in the second bucket, and so forth.

In some embodiments, a summary for a bucket may be written to a summaryfile (or directory). For example, a report summary for the averagee-mail size indicated by events in the first bucket may be written to afirst summary file, a report summary for the average e-mail sizeindicated by events in the second bucket may be written to a secondsummary file, and so forth. In some embodiments, the summary files canbe stored locally, in a memory of the indexer. In some embodiments, abucket can be associated with different types of summaries. Continuingwith the above example, in addition to the first and second summaryfiles relating to e-mail size, a report summary for the count of e-mailsprocessed between 12 pm and 1 pm from events in the first bucket may bewritten to a third summary file, a report summary for the count ofe-mails processed between 12 pm and 1 pm from events in the secondbucket may be written to a fourth summary file, a data model summary fore-mails processed by a particular e-mail server including the fields ofdate and size for events in the first bucket can be written to a fifthsummary file, a data model summary for e-mails processed by the e-mailserver including the fields of date and size for events in the secondbucket can be written to a sixth summary file, and so forth.

In some embodiments, the concurrent execution of processes generatingthe same summary is facilitated by use of summary identifiers (IDs)and/or bucket IDs. As describe herein, use of these can help to ensurethat two different processes that are generating the same type ofsummary are not operating concurrently on the same data. That is, forexample, two processes are not generating conflicting or redundantsummaries for the same bucket.

FIGS. 1A-1C are block diagrams that illustrates summarization processes10 writing to respective summaries (e.g., summary files) 20 fordifferent buckets 30. If an indexer receives a first summary request fora first type of summary associated with a summary ID of “1”, then theindexer may determine where the relevant data to be summarized islocated. If the indexer determines that the data to be summarized islocated in at least the first, second, and third buckets 30 a, 30 b and30 c of the index managed by the indexer, then the indexer may launchtwo or more processes 10 for concurrently summarizing the data in thefirst, second, and third buckets 30 a, 30 b and 30 c. For example, theindexer may launch a first process 10 a to generate the first type ofsummary for the first, second, and third buckets. Referring to FIG. 1A,the first process 10 a may proceed to generate the first type of summaryfor the first bucket 30 a, including storing the data of the summary ina first summary file 20 a associated with a bucket ID of 1 and a summaryID of 1. The first process 10 a may also lock the first summary file 20a so that other processes 10 are inhibited from writing to the firstsummary file 20 a. Shortly after the first process 10 a starts tosummarize the first bucket 30 a, the indexer may receive another summaryrequest for the first type of summary associated with a summary ID of“1” and the indexer may launch a second process 10 b to summarize thefirst, second, and third buckets 30 a, 30 b and 30 c. The second process10 b may attempt to access the first summary file 20 a (associated witha bucket ID of 1 and a summary ID of 1) in an effort to summarize thefirst bucket 30 a and determine that it is currently locked by the firstprocess 10 a. In response to determining that the first summary file 20a is locked, the second process 10 b may attempt to generate the firsttype of summary for the second bucket 30 b. This can include the secondprocess 10 b determining that a second summary file 20 b associated witha bucket ID of 2 and a summary ID of 1 is not currently locked byanother process 10 (or has not yet been generated) and storing the datafor the summary in the second summary file 20 b. The second process 10 bmay also lock the second summary file 20 b so that other processes 10(e.g., including the first process 10 a) are inhibited from writing tothe second summary file 20 b.

Referring to FIG. 1B, when the first process 10 a has completedsummarizing the first bucket 30 a, including completing writing of thesummary data for the first bucket 30 a to the first summary file 20 a,the first process 10 a may unlock the first summary file 20 a andproceed to summarizing another bucket 30 that remains to be summarized(e.g., the second bucket 20 b or the third bucket 20 c). In a mannersimilar to that described above with regard to the second process 10 b,if the first process 10 b now attempts to summarize the second bucket 30b (while the second process 10 b is still summarizing the data of thesecond bucket 30 b), the first process 10 a may attempt to access thesecond summary file 20 b (associated with a bucket ID of 2 and a summaryID of 1) and determine that the second summary file 20 b is currentlylocked by the second process 10 b. In response to determining that thesecond summary file 20 b is locked, the first process 10 b may proceedto generating the first type of summary for the third bucket 30 c. Thiscan include the first process 10 a determining that a third summary file20 c for the (associated a bucket ID of 3 and a summary ID of 1) is notcurrently locked by another process 10 (or has not yet been generated)and storing the data for the summary in the third summary file 20 c. Thefirst process 10 a may also lock the third summary file 20 c so thatother processes 10 (e.g., including the second process 10 b) areinhibited from writing to the third summary file 20 c.

In some embodiments, writing to summary 20 for a bucket 30 can includewriting an indication of whether all of the contents of the bucket 30have been summarized and/or an indication of what contents of the bucket30 have been summarized. For example, if writing to the bucket 30 a hasbeen completed and the first process 10 a is able to summarize all ofthe contents of the bucket 30 a and determine that writing of data tothe bucket 30 a is complete, then the first process 10 a may write abucket summary complete indicator (e.g., set a summary complete flag) inthe summary data. Thus, for example, a process 10 that subsequentlyattempts to summarize the bucket 30 a may determine that the summary forthe bucket 30 a is complete and may move on to summarizing anotherbucket 30. As a further example, if writing to the bucket 30 a has notyet been completed and the first process 10 a is able to summarize thecurrent contents of the bucket 30 a (e.g., about 3,000 events currentlystored in the bucket 30 a) and determine that writing of data to thebucket 30 a is not complete, then the first process 10 a may write inthe summary data a bucket summary incomplete indicator (e.g., set asummary incomplete flag) and/or summary location of (e.g., a location of3,000 indicating that the current summary 20 a for the bucket 30 a onlysummarized the first 3,000 events in the bucket 30 a). Thus, forexample, a process 10 that subsequently attempts to summarize the bucket30 a may determine that the at least a portion of the summary 20 a forthe bucket 30 a has already been completed, process the un-summarizedportion of the bucket 30 a (e.g., the events after the first 3,000events in the bucket 30 a) and update the summary 20 a accordingly.Thus, a process 10 may not have to re-summarize the data in a bucket 30that has already been summarized by another process 10.

The first and second processes 10 a and 10 b may continue to execute inthis “leap-frog” manner until they have each addressed all of thebuckets they were assigned to summarize. For example, when finishedsummarizing the second bucket 30 b, including completing writing of thesummary data for the second bucket 30 b to the second summary file 20 band unlocking the second summary file 20 b, the second process 10 b mayattempt to summarize another bucket 30 assigned to it that remains to besummarized (e.g., the third bucket 30 c). In a similar manner asdescribed above, if the second process 10 b now tries to summarize thethird bucket 30 c (while the first process 10 a is still summarizing thedata of the third bucket 30 c), the second process 10 b may attempt toaccess the third summary file 20 c (associated with a bucket ID of 3 anda summary ID of 1) and determine that the third summary file 20 c iscurrently locked by the first process 10 a. In response to determiningthat the second summary file 20 c is locked, the second process 10 b maydetermine that all of the buckets 10 that is was assigned to summarize(e.g., the first, second, and third buckets 30 a, 30 b and 30 c) havebeen addressed (e.g., based on the fact that it has generated acorresponding summary 20 b with the ID 1 for the second bucket 30 b, andit encountered the locked first and third summaries 20 a and 20 bindicating that another process 10 is generating or has alreadygenerated a summary 20 with the ID 1 for the first and third buckets 30a and 30 c). The second process 10 b may terminate in response to thisdetermination. Further, once finished summarizing the third bucket 30 c,including completing writing of the summary data for the third bucket 30c to the third summary file 20 c and unlocking the third summary file 20c, the first process 10 a may determine that all of the buckets 30 thatis was assigned to summarize have been addressed (e.g., based on thefact that it has generated a corresponding summary 20 with the ID 1 forthe first and third buckets 30 a and 30 c, and it encountered the lockedsecond summary 20 b indicating that another process 10 is generating orhas already generated a summary with the ID 1 for the second bucket 30b). The first process 10 a may terminate in response to thisdetermination.

In some embodiments, processes for generating summaries of data may notbe inhibited from summarizing the data while another process isgenerating a different summary of the same data. Continuing with theabove example described with regard to FIG. 1C, if the indexer receivesa request for a second type of summary associated with a summary ID of“2”, then the indexer may determine that the data to be summarized islocated in at least the first and third buckets 30 a and 30 c of theindex managed by the indexer. The indexer may then launch a thirdprocesses 10 c to generate the second type of summary for the first andthird buckets 30 a and 30 c. The third process 10 c may proceed togenerating the second type of summary for the first bucket 30 a,including determining that a fourth summary file 20 d (associated abucket ID of 1 and a summary ID of 2) is not currently locked by anotherprocess 10 (or has not yet been generated) and storing the data for thesummary in the fourth summary filed 20 d. The third process 10 c mayalso lock the fourth summary file 20 d so that other processes 10 (e.g.,such as subsequent process 10 for the second type of summary) areinhibited from writing to the fourth summary file 20 c. Shortly afterthe third process 10 c starts to summarize the first bucket 30 a, theindexer may receive another request for a second type of summaryassociated with a summary ID of “2”, and the indexer may launch tosummarize the first and third bucket 30 a and 30 c. The fourth process10 d may attempt to access the fourth summary file 20 d (associated witha bucket ID of 1 and a summary ID of 2) in an effort to summarize thefirst bucket 30 a and determine that it is currently locked by the thirdprocess 10 c. In response to determining that the fourth summary file 20d is locked, the fourth process 10 d may proceed to generating thesecond type of summary for the third bucket 30 c. This can include thefourth process 10 d determining that a fifth summary file 20 eassociated a bucket ID of 3 and a summary ID of 2 is not currentlylocked by another process 10 (or has not yet been generated) and storingthe data for the summary in the fifth summary file 20 e. The fourthprocess 10 d may also lock the fifth summary file 20 e so that otherprocesses 10 (e.g., including the third process 10 c) are inhibited fromwriting to the fifth summary file 20 e. When each of the third andfourth processes 10 c and 10 d determine that all of the buckets theywere assigned to summarize have been addressed, they may terminate.

In some embodiments, a maximum number of parallel processes can bedefined for an indexer. The maximum number of parallel process maydefine a maximum number of summarization processes that an indexer canexecute concurrently. Continuing with the above example, if a user(e.g., an administrator) set a maximum number of parallel process forthe indexer to 3, then the indexer may execute the first, second andthird processes 10 a, 10 b and 10 c in parallel, but may not be allowedto execute the fourth process 10 d until after at least one of thefirst, second and third processes 10 a, 10 b and 10 c completes or isotherwise terminated. As described herein, in some embodiments, ascheduling entity, such as a search head, may employ a maximum number ofparallel processes via the scheduling and/or sending of summary requestto an indexer. For example, a search head may not send a summaryrequests to an indexer if the indexer already executing the maximumnumber of processes.

In some embodiments, a minimum process delay for initiating parallelprocess can be defined for an indexer. The minimum process delay maydefine a minimum time between starting parallel processes. For example,minimum process delay of 5 minutes for an indexer would indicate that anindexer is not allowed to launch a process within 5 minutes afterlaunching another process. Continuing with the above example, if a user(e.g., an administrator) set a minimum process delay of 5 minutes forthe indexer, then the indexer may not be allowed to launch the secondprocess 10 b until at least 5 min have passed since the first process 10a was launched. As described herein, in some embodiments, a schedulingentity, such as a search head, may employ a minimum process delay viathe scheduling and/or sending of summary requests to an indexer. Forexample, a search head may ensure that at least the minimum delay occursbetween the sending of summary requests to an indexer.

Accordingly, embodiments may enable multiple summarization processes toexecute in parallel without generating conflicting or redundant summarydata. This may enable summarization processes to be completed in arelatively fast and efficient manner. In some embodiments, as discussedabove, summarization processes can be executed by an indexer of a largerdata processing system, such as a SPLUNK® ENTERPRISE system produced bySplunk Inc. of San Francisco, Calif., to store and process performancedata, described in more detail herein with regard to at least FIGS. 1and 5-11D. Further, the resulting summaries (e.g., the bucket levelsummaries) can be used, for example, in report acceleration and/or datamodel acceleration processes. For example, in the context of a largerdata processing system, such as a SPLUNK® ENTERPRISE system, bucketsummaries for each of one or more buckets of one or more indexers can beemployed by a search head to generate reports, search results, and thelike for the larger set of data contained in the buckets.

FIG. 2 illustrates an example data processing environment(“environment”) 100 in accordance with the disclosed embodiments. Insome embodiments, the environment 100 can include an event-processingsystem (“system”) 102 communicatively coupled to one or more clientdevices 104 via a communications network 106. The client device 104 maybe used or otherwise accessed by a user 108, such its a systemadministrator or a customer.

In some embodiments, the system 102 can include an application server110, one or more data sources (“sources”) 112, one or more forwarders114, one or more indexers 116, one or more index data stores 118, and/orone or more search heads 120. As described herein, data may be indexedand stored in one or more indexes 140. An index 140 can include alogical grouping of data (e.g., having common characteristics). Asfurther described herein, an index 140 can include one or more buckets30, and each of the buckets 30 can include an index file 144 and/or araw data file 146. A raw data file 146 may include raw source data 130in compressed form. An index file 144 may include index data that pointsto the location of certain data within the raw data file 146.

The network 106 may include an element or system that facilitatescommunication between the entities of the environment 100 (e.g.,including the application server 110 and the client devices 104). Thenetwork 106 may include an electronic communications network, such asthe Internet, a local area network (LAN), a wide area network (WAN), awireless local area network (WLAN), a cellular communications network,and/or the like. In some embodiments, the network 106 can include awired or a wireless network. In some embodiments, the network 106 caninclude a single network or a combination of networks.

A client device 104 may include any variety of electronic devices. Insome embodiments, a client device 104 can include a device capable ofcommunicating information via the network 106. A client device 104 mayinclude one or more computer devices, such as a desktop computer, aserver, a laptop computer, a tablet computer, a wearable computerdevice, a personal digital assistant (PDA), a smart phone, and/or thelike. In some embodiments, a client device 104 may be a client of theapplication server 110. In some embodiments, a client device 104 caninclude various input/output (I/O) interfaces, such as a display (e.g.,for displaying a graphical user interface (GUI)), an audible output userinterface (e.g., a speaker), an audible input user interface (e.g., amicrophone), an image acquisition interface (e.g., a camera), akeyboard, a pointer/selection device (e.g., a mouse, a trackball, atouchpad, a touchscreen, a gesture capture or detecting device, or astylus), and/or the like. In some embodiments, a client device 104 caninclude general computing components and/or embedded systems optimizedwith specific components for performing specific tasks. In someembodiments, a client device 104 can include programs/applications thatcan be used to generate a request for content, to provide content, torender content, and/or to send and/or receive requests to and/or fromother devices via the network 106. For example, a client device 104 mayinclude an Internet browser application that facilitates communicationwith the application server 110 via the network 106. In someembodiments, a program, or application, of a client device 104 caninclude program modules having program instructions that are executableby a computer system to perform some or all of the functionalitydescribed herein with regard to at least client device(s) 104. In someembodiments, a client device 104 can include one or more computersystems similar to that of the computer system 1000 described below withregard to at least FIG. 4.

The application server 110 may include a computing device having networkconnectivity and being capable of providing one or more services tonetwork clients, such as a client device 104. These services may includeingesting, processing, storing, monitoring, and/or searching data.Although certain embodiments are described with regard to a singleserver for the purpose of illustration, embodiments may includeemploying multiple servers, such as a plurality of distributed servers.In some embodiments, the application, server 110 can include one or morecomputer systems similar to that of the computer system 1000 describedbelow with regard to at least FIG. 4.

A data source (also referred to as a “source” or “data input”) 112 maybe a source of incoming source data (also referred to as “event data”)130 being fed into the system 102. A data source 112 may include one ormore external data sources, such as web servers, application servers,databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,and/or the like. A data source 112 may be located remote from the system102. For example, a data source 112 may be defined on an agent computeroperating remote from the system 102, such as on-site at a customer'slocation, that transmits source data 130 to one or more forwarders 114via a communications network (e.g., network 106). The source data 130can be a stream or set of data fed to an entity of the system 102, suchas a forwarder 114. The source data 130 may include, for example, rawmachine-generated time-series data, such as server log files, activitylog files, configuration files, messages, network packet data,performance measurements, sensor measurements, and/or the like. A datasource 112 may be local to (e.g., integrated with) the system 102. Forexample, a data source 112 may be defined on a forwarder 114. In someembodiments, the data sources 112 can be the same or similar to the datasources 1105 described below with regard to at least FIG. 5. In someembodiments, a source 112 can include one or more computer systemssimilar to that of the computer system 1000 described below with regardto at least FIG. 4.

A forwarder 114 may be an entity of the system 102 that forwards data toanother entity of the system, such as an indexer 116, another forwarder114, or a third-party system. An entity that receives data from aforwarder 114, such as an indexer 116, may be referred to as a receiverentity. In some embodiments, the forwarders 114 can be the same orsimilar to the forwarders 1101 described below with regard to at leastFIG. 5. In some embodiments, a forwarder 114 can include one or morecomputer systems similar to that of the computer system 1000 describedbelow with regard to at least FIG. 4.

An indexer 116 may be an entity of the system 102 that indexes rawsource data 130, transforming into events and placing the results intoan index 140. An indexer 116 may also search indexes 140 in response tosearch requests. An indexer 116 may perform other functions, such asdata input and search management. In some instances, the forwarders 114handle data input, and forward the source data 130 to the indexers 116for indexing. An indexer 116 may perform searches across its own storeddata (e.g., the data of indexes 140 stored in an index data store 118managed by the indexer 116). In some instances, a search head 120 canhandle search management and coordinate searches across multipleindexers 116. In some embodiments, the indexer 116 can be the same orsimilar to the indexers 1102 described below with regard to at leastFIG. 5. In some embodiments, an indexer 116 can include one or morecomputer systems similar to that of the computer system 1000 describedbelow with regard to at least FIG. 4.

The process of indexing data may be part of a larger sequence ofprocessing data. In some embodiments, a “data pipeline” refers to aroute that data takes through an event-processing system 102, from itsorigin in sources, such as log files and network feeds, to itstransformation into searchable events that encapsulate valuableknowledge. Such a data pipeline may include, for example, an inputsegment, a parsing segment, an indexing segment, and a search segment.Each segment may be conducted by one or more entities of the system 102,such as one or more forwarders 114, one or more indexers 116, and/or oneor more search heads 120 of system 102.

During the input segment (e.g., the first segment of the data pipeline),system 102 may acquire a raw data stream (e.g., source data 130) fromits source (a source 112), break it into blocks (e.g., 64K blocks ofdata), and/or annotate each block with metadata keys. After the data hasbeen input, it may be moved to the next segment of the pipeline(parsing). The data input segment may be conducted, for example, by aforwarder 114 and/or an indexer 116 of system 102. In some instances, aparsing queue in the data pipeline holds data after it enters the systembut before parsing (a first phase of the event processing) occurs. Thus,incoming data may first go to the parsing queue and from there to theparsing segment.

During the parsing segment (e.g., the second segment of the datapipeline), system 102 may conduct parsing, a first stage of the eventprocessing of the raw data (e.g., source data 130). This can include,for example, extracting a set of default fields for each event,including host, source and source type, configuring character setencoding, identifying line termination using line breaking rules,identifying event boundaries, identifying event timestamps (or creatingthem if they don't exist), masking sensitive event data (such as creditcard or social security numbers), applying custom metadata to incomingevents, and/or the like. Accordingly, during this first stage of eventprocessing, the raw data may be data broken into individual events, andthe parsed data may be moved to the next segment of the pipeline(indexing). The parsing segment may be conducted, for example, by aheavy forwarder 114 and/or an indexer 116 of system 102.

During the indexing segment (e.g., the third segment of the datapipeline), system 102 may perform indexing of the parsed data, a secondstage of the event processing. This can include, for example, breakingall events into segments that can then be searched upon, building theindex data structures, and writing the raw data and index files to disk,where post-indexing compression occurs. Accordingly, during the secondstage of the event processing, the parsed data (also referred to as“events”) may be written to a search index on disk (e.g., written to anindex 140 in an index data store 118). The indexing segment may beconducted, for example, by an indexer 116 of system 102. In someembodiments, both parsing and indexing can take place on the sameindexer 116. In some embodiments, an index queue in the data pipelineholds parsed events waiting to be indexed. Thus, incoming data may gofrom the parsing queue to the parsing segment of the pipeline where itundergoes parsing, and the processed data may move to the index queueand ultimately on to the indexing segment, where the index is built.

During the searching segment (e.g., the fourth segment of the datapipeline), system 102 may conduct searches of the indexed data toidentify and access data that is responsive to search queries. This caninclude, for example, identifying stored events in a raw data that areresponsive to user specified search criteria. For example, if indexeddata is stored in a data store 118 of an indexer 116, and the indexer116 is assigned a search task by the search head 120, then the indexer116 may search the data store 118 for data responsive to the search andsend any responsive data back to the search head 120. The search head120 may send similar search tasks to other indexers 116, consolidate theresponsive data received from the indexers 116, and provide theconsolidated search results (e.g., to a client device 104 for display toa user).

An index data store 118 may include a medium for the storage of datathereon. For example, a data store 118 may include a non-transitorycomputer-readable medium storing data thereon that is accessible byentities of the environment 100, such as the corresponding indexer 116.The data may include, for example, one or more indexes 140 including oneor more buckets 30, and the buckets 30 may include an index file 144and/or a raw data file 146 (e.g., including parsed, time-stampedevents). In some embodiments, each data store 118 is managed by a givenindexer 116 that stores data to the data store 118 and/or performssearches of the data stored on the data store 118. Although certainembodiments are described with regard to a single data store 118 for thepurpose of illustration, embodiments may include employing multiple datastores 118, such as a plurality of distributed data stores 118. In someembodiments, an index data store 118 is the same or similar to the datastores 1103 described below with regard to at least FIG. 5.

A bucket 30 may be a directory or partition containing part of an index140. A bucket 30 may contain both the raw data file 146 and acorresponding set of index files 144. A raw data file 146 may be acompressed file in an index bucket 142 that contains event data, as wellas journal information that the indexer can use to reconstitute theindex's metadata files (“index files”). For example, an indexer 116 mayreceive machine-generated raw source data 130 including events generatedby a source 112 (e.g., log data from an e-mail server), and the indexer116 may index and store the events in buckets 30 managed by the indexer.The events may be time stamped (or otherwise associated with a time) andthe indexer 116 may index and store the events in certain buckets 30based on the time associated with the events. For example, the indexermay index and store the first 1000 events generated by the e-mail serverin a first bucket 30, index and store the second 1000 events generatedby the e-mail server in a second bucket 30, and so forth. Thus, each ofthe buckets 30 may store a subset of events of a similar age, or atleast being associated with a given timespan (e.g., a timespan from thetime associated with the earliest event in the bucket 30 to the timeassociated with latest event in the bucket 30). In such an embodiment,the first bucket 30 may include a first raw data file 146 that is acompressed file including the data of the first 1000 events generated bythe e-mail server, and a first index file 144 that includes the metadatafor the 1000 events stored in the first bucket 30. Similarly, the secondbucket 30 may include a second raw data file 146 that is a compressedfile including the data of the first 1000 events generated by the e-mailserver, and a second index file 144 that includes the metadata for the1000 events stored in the second bucket 30.

An index 140 may consist of one or more buckets 30 organized, forexample, by age, and which may roll through one or more stages in theirtransition to retirement and eventual archiving or deletion. The stagesmay include, for example, a hot stage, a warm stage, a cold stage, afrozen stage, and a thawed stage. The data may transition across thestages in the following order as the data ages: hot, warm, cold, frozen,thawed. Each of the stages may be associated with a corresponding bucketthat holds data for that stage. A hot bucket 30 may be a location tostore data subject to intensive read and write operations, e.g., wherethe indexing occurs. A warm bucket 30 may be a location to store datasubjected to mostly read and optimization operations. A cold bucket 30may be a location to store data subjected to search operations. A frozenbucket 30 may be a location to store data that is queued for deletion orarchiving. A thawed bucket 30 may be used to re-import data from frozenbuckets. Freshly indexed data may start out in a hot bucket 30 that isactively being written to. The data of a hot bucket 30 may be storedtemporarily in a memory location, such as in flash memory of the indexer116. When the hot data bucket 30 reaches a specified size or age, thebucket 30 may be transitioned into (or “rolled to”) a warm, data bucket30, and a new hot data bucket 30 may be created. Similar to the hotbucket 30, the data of a warm bucket 30 may be stored temporarily in amemory location, such as flash memory of the indexer 116. After sometime, the bucket 30 may be transitioned into (or “rolled to”) a coldbucket 30. The data of a cold bucket 30 may be stored in a morepermanent location that remains searchable, such as a hard drive of theindexer 116. A cold bucket 30 may eventually transition into a frozenbucket 30 that is later deleted or archived. The data of a frozen bucket30 may be stored an archive that is not readily searchable, such as atape drive of the indexer 116. If data is to be returned to a coldbucket 30, it may be moved in a thawed bucket 30, during its transitionfrom the frozen bucket 30 to the cold bucket 30, such as during itstransition from a tape drive to the hard drive. The states of variousbuckets 30 and/or the discarding or archiving of data for an index 140may be based on data retention settings specified by an index definitionfor the index 140.

In some embodiments, an index 140 can include one or more summaries(also referred to as “bucket summaries”) 20 for one or more or more ofthe buckets 30 of the index 140. A bucket summary 20 can include abucket summary ID 150, a bucket ID 152 and/or summary data 154. A bucketsummary may include a file or directory in the data store 118 of theindexer. Although the depicted embodiment illustrates the bucketsummaries 20 store separately from the buckets 30, in some embodiments,a bucket summary 20 may be stored in the bucket (e.g., in the partitionincluding the raw data file 146 and the index file for the bucket 30that the summary corresponds to). The summary ID 150 may identify theparticular summary being generated. For example, bucket summaries 20 forsummarizing e-mail size (e.g., including a count and total size fore-mail processed by an e-mails server) may have a summary ID 150 of “1”,bucket summaries 20 for summarizing e-mail count (e.g., including acount of e-mails processed by the e-mail server between 12 pm and 1 pm)may have a summary ID of “2”, and so forth. The bucket ID 150 mayidentify the particular bucket for which the summary is generated. Forexample, bucket summaries 20 for summarizing events in a first bucket 30may have a bucket ID 152 of “1”, bucket summaries 20 for summarizingevents in a second bucket 30 may have a bucket ID 152 of “2”, and soforth. The summary data 154 can include the data summarizing thecontents of the corresponding bucket 30. For example, if a first bucketsummary 20 corresponds a report summary for summarizing e-mail size(e.g., including a count and total size for e-mail processed by ane-mails server) for a first bucket 30 that contains 10,000 eventsgenerated by an e-mail sever and 1000 of those events indicateprocessing of an e-mail and include a size field, then the summary data154 for the first bucket summary 20 may include a count of the eventsindicate processing of an e-mail and include a size field (e.g.,count=1000) and a sum of the values in the size fields in those events(e.g., total size=1000 MB). As described herein, such pre-computedvalues can be used, for example, to generate an average e-mail “size”for that bucket of data (e.g., average e-mail size for the firstbucket=1 MB). As a further example, if a second bucket summary 20corresponds a data model summary for identifying the size fields ofevents indicating e-mails processed by the e-mail server, and the firstbucket includes 10,000 events total, with 1,000 of the 10,000 eventsindicating e-mails processed by the e-mail server and including a fieldfor size, then the summary data 154 for the second bucket summary 20 mayinclude or otherwise identify the 1,000 events and/or the values of thesize fields of the 1,000 events.

A search head 120 may be an entity of the system 102 that handles searchrequests and/or consolidates the search results for presentation to auser. In a distributed search environment (e.g., including multipleindexers 116), a search head 120 may distribute search requests across aset of indexers 116 that perform the actual searching to generateindividual sets of search results, and then merge the individual sets ofsearch results into a consolidated set of search results that areprovided to the user. In a non-distributed search environment (e.g.,including only a single indexer 116), the indexer 116 may assume therole of a search head 120 and may handle the search management, as wellas the indexing and searching functions. An entity of the system 102 mayfunction as both a search head 120 and a search peer. If an entity doesonly searching (and not any indexing), it is usually referred to as adedicated search head. A search head cluster may be a group of searchheads 120 that serve as a central resource for searching. In someembodiments, a search head 120 is the same or similar to the search head1104 described below with regard to at least FIG. 5. In someembodiments, the search head 120 can include one or more computersystems similar to that of the computer system 1000 described below withregard to at least FIG. 4.

In some embodiments, a search head 120 may distribute summary tasks toone or more indexers 116. For example, a search head 120 may determinethat a summary of a set of data needs to be generated, identify theindexers 116 that manage indexed data relevant to the summary, anddistribute one or more summary requests to each of the identifiedindexers 116. A described herein, in some embodiments, the search head120 may send summary request to an in accordance with configurations ofthe indexer 116, such as the maximum number of parallel process, minimumprocess delay, and/or the like for the indexer 116. The indexers 116may, in turn, execute one or more processes (e.g., including parallelsummary processes 10) to generate corresponding bucket summaries. Forexample, a search head 120 may determine that a summary of the size ofe-mails processed by a particular e-mail server needs to be generated(e.g., including a count and total size for e-mails processed the e-mailserver), the search head 120 may identify a ten indexers 116 that havelog-data from the e-mail server, and distribute one or morecorresponding summary requests to each of the ten indexers 116. Each ofthe indexers 116 may, in turn, execute one or more processes (e.g.,including parallel summary processes 10) to generate correspondingbucket summaries 20 (e.g., each bucket summary 20 including a count andtotal size for e-mails processed the e-mail server determined from theevents stored in the bucket 30 corresponding to the bucket summary 20).As a further example, a search head 120 may determine that a data modelsummary for e-mails processed by a particular e-mail server includingthe date field needs to be generated, the search head 120 may identifyten indexers 116 that have log-data from the e-mail server, anddistribute one or more corresponding summary requests to each of the tenindexers 116. Each of the indexes 116 may, in turn, execute one or moreprocesses (e.g., including parallel summary processes 10) to generatecorresponding bucket summaries 20 (e.g., each bucket summary 20including or otherwise identifying the events and/or the values of thesize fields of the events generated by the e-mail server from tireevents stored in the bucket 30 corresponding to the bucket summary 20).

In some embodiments, a search head may combine bucket summaries 20 fromsome or all of the indexers 116 in a system. For example, during reportacceleration process, a search head 120 may request the above describedreport bucket summaries 20 from the ten indexers 116, and combine countsof events and size values from all of the bucket summaries 20 togenerate an average e-mail size indicated by the larger set of data(e.g., the average e-mail size from the events indicated by the eventsstored the buckets 30 of the ten indexers 116). This average value may,for example, be provided to the search server 110 for presentation tothe user 108. For example, the application server 110 may serve, to theclient device for display to the user 108, GUI content 156 including theaverage e-mail size. As a further example, during a data modelacceleration process, a search head 120 may request the above describeddata model bucket summaries 20 from the ten indexers 116, and combinethe events and/or values of the size fields from all of the bucketsummaries 20 to generate full set of events and/or values of the sizefields. The events and/or filed values of the combined data modelsummary may, for example, be provided to the search server 110 forpresentation to the user 108. For example, the application server 110may serve, to the client device for display to the user 108, GUI content156 including a pivot visualization of the data (e.g., a visualizationof the events and/or filed values of the combined data model summary).Such data model reporting and visualizations are described in U.S.patent application Ser. No. 14/503,335 titled “Generating Reports fromUnstructured Data” and filed on Sep. 30,2014, which is herebyincorporated by reference in its entirety.

FIG. 3A is a flowchart that illustrates an example method 300 forgenerating summary requests in accordance with the disclosedembodiments. Some or all of the elements of the method 300 may beperformed, for example, by one or more scheduling entities of the system102, such as one or more search heads 120. Method 300 may includeidentifying a summary to be generated (block 302). For example, a searchhead 120 may determine that a report summary (e.g., including a countand total size for e-mails processed the e-mail server) needs to begenerated in response to a user enabling acceleration of a report on theaverage size of e-mails processed by an e-mail server. As a furtherexample, the search head 120 may determine that a data model summary(e.g., for the date field of e-mails processed by the e-mail server)needs to be generated in response to a user enabling acceleration of adata model for the date field of e-mails processed by the e-mail server.

Method 300 may include identifying one or more indexers for serving thesummary request (block 304). In some embodiments identifying indexersfor serving the summary request can include identifying one or moreindexers 116 that manage data stores holding data (e.g., events)relevant to the summary to be generated. Continuing with the aboveexample, the search head 120 may identify ten indexers 116 (e.g.,including a first indexer 116) that have log-data from the e-mailserver.

Method 300 may include scheduling the summary request (block 306). Insome embodiments scheduling the summary request can include assessingthe processing load of the identified indexers, and determining aschedule for sending summary requests to the indexers 116 based on theprocessing loads. This can include determining, for each of the indexers116, whether a maximum number of parallel processes are currentlyexecuting and/or a minimum process delay has been satisfied. In someembodiments, determining whether a maximum number of processes arecurrently executing on an indexer 116 can include the search head 120determining whether the number of summary processes currently beingexecuted by the indexer 116 is equal to or greater than the maximumnumber of processes (e.g., specified in an index configuration file) forthe indexer 116. If the indexer 116 is configured to handle a maximum of5 concurrent summarization processes, for example, and 5 processes arecurrently being executed by the first indexer 116, then the search head120 may determine that the maximum number of processes is not currentlybeing executed. Conversely, if 10 processes are currently being executedby the first indexer 116, then the search head 120 may determine thatthe maximum number of processes is currently being executed. In someembodiments, determining whether a minimum process delay has beensatisfied for an indexer 116 can include the search head 120 determiningwhether a time equal to or greater than the minimum delay for an indexer116 (e.g., specified in an index configuration file for the indexer 116)has passed since the last summarization process was launched by theindexer 116. If the indexer 116 is configured to have a minimum delay of5 minutes between launching summarization processes 10, and the lastprocess was launched on the indexer 116 6 minutes ago, then the searchhead 120 may determine that the minimum process delay has beensatisfied. Conversely, if the last process was launched 3 minutes ago,then the search head 120 may determine that the minimum process delayhas not been satisfied. In some embodiments, the search head maydetermine a process to have been launched at about the time of thesearch head sent a corresponding request to launch the process.

In response to determining that the maximum number of processes arecurrently executing on an indexer 116 and/or determining that theminimum process delay has not been satisfied, the search head 120 maynot schedule a summary request to be sent immediately, but may insteadschedule a summary request to be sent at a later time, when the minimumprocess delay has been satisfied and less than the maximum number ofprocesses are currently executing on the indexer 110. That is, thesearch head 120 may schedule the summary request to be sent when bothconditions are satisfied. If for example, the search head 120 determinesthat no processes are currently being executed on an indexer 116, thesearch head may schedule a summary request (e.g., a report summaryrequest for a count and total size for e-mails processed the e-mailserver and/or a data model request for the date field of e-mailsprocessed by the e-mail server) to be sent every 5 minutes to each ofthe indexers 116, up until ten processes are executing concurrently eachof the respective indexers 116 or the summarization tasks is terminatedcompleted (e.g., the user disables the corresponding reportacceleration).

Method 300 may include sending summary request to the indexers (block306). In some embodiments sending summary request to the indexers caninclude sending summary requests to the identified indexers 116 inaccordance with the determined schedule for summary requests. Forexample, if ten indexers are identified that do not have any summaryprocesses being executed and prior summary processes on each of theindexers were generated more than 5 minutes ago, the search head 120 maysend summary requests (e.g., a report summary request for a count andtotal size for e-mails processed the e-mail server and/or a data modelrequest for the date field of e-mails processed by the e-mail server) toeach of the indexers 116 every 5 minutes, up until ten processes areexecuting concurrently each of the respective indexers 116 or thesummarization tasks is terminated or otherwise completed (e.g., the userdisables the corresponding report acceleration). In some embodiments,each of the summary requests may be associated with a summary ID. Forexample, the report summary request for a count and total size fore-mails processed the e-mail server may be associated with a summary IDof “1”, a data model summary request for a data model summary for thedate field of e-mails processed by the e-mail server may be associatedwith a summary ID of “3”, and so forth. The summary ID may be includedin the summary request. For example, the report summary request for acount and total size for e-mails processed the e-mail server may includea summary ID of “1” in the request.

FIG. 3B is a flowchart that illustrates an example method 320 forlaunching summary requests in accordance with the disclosed embodiments.Some or all of the elements of the method 320 may be performed, forexample, by one or more indexers 116. Method 320 may include receiving asummary request (block 322). In some embodiments, receiving a summaryrequest can include an indexer 116 receiving a summary request from asearch head 120. For example, a first of the ten indexers 116 mayreceive a report summary request (e.g., including a count and total sizefor e-mails processed the e-mail server) from the search head every 5minutes up until ten processes are executing concurrently on the indexer116 or the summarization tasks is terminated completed.

Method 320 may include identifying buckets for servicing the summaryrequest (block 324). In some embodiments, identifying buckets forservicing the summary request can include the first indexers 116identifying one or mere of the buckets 30 in the index 140 managed bythe indexer 116 (e.g., buckets 30 in the data store 118 for the indexer116) that include one or more events that are relevant to the summaryrequest. Continuing with the above examples of report summary requests(e.g., relating to a report summary request for a count and total sizefor e-mails processed the e-mail server or a data model request for thedate field of e-mails processed by the e-mail server), the first indexer116 may identify three of its buckets 30 (e.g., first, second and thirdbuckets 30) as containing events of log data received from the e-mailserver that are relevant to the request. Continuing with the aboveexample regarding the request for a data model summary for the datefield of events for e-mails processed by the e-mail server, the firstindexer 116 may identify three of its buckets 30 (e.g., the first,second and third buckets 30) as containing events of log data receivedfrom the e-mail server.

Method 320 may include launching a summary process (block 326). In someembodiments, launching a summary process includes the indexer 116launching a process corresponding to the received summary request. Forexample, if the received summary request includes a report summaryrequest for a count and total size for e-mails processed the e-mailserver and associated with a summary ID of “1” and the indexeridentifies first, second and third buckets 30 in its index 140 ascontaining relevant data (e.g., events of log data received from thee-mail server), then the indexer 116 may initiate execution of a processassigned to generate corresponding report bucket summaries with asummary ID of “1” for each of the first, second and third buckets 30 inits index 140. Similarly, if the received summary request includes adata model summary request for the date field of events for e-mailsprocessed by the e-mail server and associated with a summary ID of “3”and the indexer 116 identifies the first, second and third buckets 30 inits index 140 as containing relevant data (e.g., events of log datareceived from the e-mail server), then the indexer 116 may initiateexecution of a process assigned to generate corresponding data modelbucket summaries with a summary ID of “3” for each of the first, secondand third buckets 30 in its index 140. A process that is the same orsimilar to that described with regard to method 320 may be executed bythe indexer 116 for each summary request received by the indexer 116(e.g., received from a search head 120). Although certain embodimentsare described with regard to identifying buckets for servicing thesummary request (block 324) and, then, launching a summary process(block 326), embodiments can include executing these elements in anysuitable order. For example, a summary process may be launched beforeidentifying the buckets for servicing the summary request areidentified. That is, for example, launching a summary process (block326) may occur before identifying buckets for servicing the summaryrequest (block 324).

FIG. 3C is a flowchart that illustrates an example method 350 forexecuting summarization processes in accordance with the disclosedembodiments. Some or all of the elements of the method 350 may beperformed, for example, by one or more processes launched or otherwiseexecuted by an indexer 116. The method 350 may provide a summary processfor summarizing buckets of data concurrently (e.g., in parallel) withother summary processes. Method 300 may include identifying a bucket tobe summarized (block 352). In some embodiments, identifying a bucket tobe summarized can include the process identifying one of one or more ofthe buckets it is assigned to summarize that has not yet been addressedby the process (e.g., the process has not yet summarized the bucket 30,and the process has not yet encountered as summary for the bucket 30that is locked or a summary complete flag indicating that the bucket 30is completely summarized). Continuing with the above example, andreferring to FIGS. 1A-1D, if a first process 10 a is assigned tosummarize the first, second, and third buckets 30 a, 30 b and 30 c, forexample, the first process 10 a may identify the first bucket 30 a in afirst iteration (on a first pass through block 352), identify the secondbucket 30 b in a second iteration (on a second pass through block 352),and identify the third bucket 30 c in a third iteration (on a secondpass through block 352).

Method 300 may include determining whether a summary directory for thesummary to write to exists and is locked (block 354). In someembodiments, determining whether a summary directory for the summary towrite to exists and is locked can include the process determiningwhether a bucket summary file 20 corresponding to the identified bucket30 to be summarized exists and is already locked (e.g., by anothersummary process). Continuing with the above example, and referring toFIGS. 1A-1D, if a first process 10 a is tasked with generating a reportsummaries having a summary ID of “1”, and the first bucket 30 a isidentified to be summarized, the first process 10 a may determinewhether a first bucket summary file 20 a (having a bucket ID 152 of 1and a summary ID 150 of 1) exists and is locked (e.g., by anothersummary process 10). If the file does not exist or no other summaryprocess 10 is currently writing to the first summary 20 a, it may bedetermined that the file 20 a is not locked. Conversely, if the filedoes exist and another summary process 10 is currently writing to thefirst summary 20 a, it may be determined that the file 20 a is locked.If it is determined that the summary directory for the summary to writeto is locked (block 354), the method 250 may include proceeding todetermining whether an additional bucket needs to be summarized (block362) (described in more detail below).

If it is determined that the summary directory for the summary to writeto is unlocked (block 354), the method 250 may include proceeding togenerating the summary for the bucket, including locking the summarydirectory (block 356), writing the summary for the bucket to the summarydirectory (block 358), and unlocking the summary directory (block 360).If a summary does not exist, the process can also include creating thesummary directory. In some embodiments, locking the summary directorycan include the process locking the summary file 20 to prevent orotherwise inhibit other processes from writing and/or reading thesummary file 20. For example, the first process 10 a may lock the firstsummary file 20 a so that other processes 10 are inhibited from writingand/or reading the first summary file 20 a. In some embodiments, writingthe summary for the bucket to the summary directory (block 358) caninclude the process 10 writing summary data 154 to the summary file 20.For example, continuing with the above example relating to the reportsummary for the size of e-mails processed by the e-mail server, thefirst process 10 a may assess the events in the first bucket 30 a,determine that there are 1000 events that include a “size” field and asum of the values for the fields in those events is 1000 MB, and writeto the summary file 20 a summary data 154 that include a correspondingcount and sum (e.g., count=1000; sum=1000 MB).

In some embodiments, writing the summary data for a bucket can includewriting an indication of whether all of the contents of the bucket 30have been summarized and/or an indication of what contents of the bucket30 have been summarized. For example, if writing to the bucket 30 a hasbeen completed (the bucket 30 a is a warm bucket) and the first process10 a is able to summarize all of the contents of the bucket 30 a anddetermine that writing of data to the bucket 30 a is complete, then thefirst process 10 a may write a bucket summary complete indicator (e.g.,set a summary complete flag) in the summary data 154 of the summary 20a. Thus, for example, a process 10 that subsequently attempts tosummarize the bucket 30 a may determine that the summary for the bucket30 a is complete and may move onto summarizing another bucket 30. As afurther example, if writing to the bucket 30 a has not yet beencompleted (the bucket 30 a is a hot bucket) and the first process 10 ais able to summarize the current contents of the bucket 30 a (e.g.,about 3,000 events currently stored in the bucket 30 a) and determinethat writing of data to the bucket 30 a is not complete, then the firstprocess 10 a may write, in the summary data 154 of the summary 20 a, abucket summary incomplete indicator (e.g., set a summary incompleteflag) and/or summary location (e.g., a location of 3,000 indicating thatthe current summary 20 a for the bucket 30 a only summarized the first3,000 events in the bucket 30 a). Thus, for example, a process 10 thatsubsequently attempts to summarize the bucket 30 a may determine thatthe at least a portion of the summary 20 a for the bucket 30 a hasalready been completed, process the un-summarized portion of the bucket30 a (e.g., the events after the first 3,000 events in the bucket 30 a)and update the summary 20 a accordingly. Thus, a process 10 may not haveto re-summarize the data in bucket 30 that has already been summarizedby another process 10.

In some embodiments, unlocking the summary directory can include theprocess 10 unlocking the summary file 20 to enable other processes 10 towrite and/or read the summary file 20. For example, the first process 10a may unlock the first summary file 20 a (e.g., now including thesummary data 154 written by the first process 10 a) so that otherprocesses 10 are allowed to write to and/or read from the first summaryfile 20 a.

In some embodiments, determining whether an additional bucket needs tobe summarized can include the process determining whether any of the oneor more of the buckets 30 the process is assigned to summarize has notyet been addressed by the process (e.g., the process has not yetsummarized the bucket 30 and the process 10 has not yet encountered alocked summary for the bucket 30 or a summary complete flag indicatingthat the bucket 30 is completely summarized). Continuing with the aboveexample, and referring to FIGS. 1A-1D, if the first process 10 a isassigned to summarize the first, second, and third buckets 30 a, 30 band 30 c, for example, and the first process 10 a has just completedsummarizing the first bucket 30 a and just encountered a locked summaryfile for 20 b for the second bucket 30 b, the process 10 a may determinethat at least the summary of the third bucket 30 c may need to becompleted (e.g., the process 10 a has not yet summarized the bucket 30 cand the process 10 a has not yet encountered a locked summary file 20for the bucket 30 or a summary complete flag indicating that the bucket30 is completely summarized). As a result, the method may proceed toidentifying the bucket to be summarized (e.g., the third bucket 30 c)(block 352). As a further example, if the first process 10 a is assignedto summarize the first, second, and third buckets 30 a, 30 b and 30 c,for example, and the first process 10 a has just completed summarizingthe first and third buckets 30 a and 30 c and encountered a lockedsummary file for 20 b for the second bucket 30 b, the process 10 a maydetermine that no additional bucket 30 needs to be summarized by theprocess. As a result, the method may complete or otherwise terminate asindicated by the “stop” block.

Accordingly, provided in some embodiments are systems and methods forconcurrent summarization of indexed data. In some embodiments, two ormore summary processes can be executed concurrently (e.g., in parallel)by an indexer 116 to generate summaries for respective subsets ofindexed data (e.g., partitions or buckets of indexed data) managed bythe indexer 116. In some embodiments, the summaries (e.g., reportsummaries and/or data model summaries) can be used to accelerate certainprocesses executed by the system 102, such as report acceleration anddata model acceleration. Such a search system can employ, for example, alate-binding schema to identify one or more event records of a set ofindexed event records that each include a portion ofraw-machine-generated data and are each time-stamped or otherwiseassociated with a particular time. At least the following sectionsdescribe an example data system that may employ the describedembodiments, including employing one or more searches ofmachine-generated data that can be employed in conjunction with theabove described techniques.

FIG. 4 is a diagram that illustrates an example computer system 1000 inaccordance with one or more embodiments. In some embodiments, thecomputer system 1000 may include a memory 1004, a processor 1006, and aninput/output (I/O) interface 1008. The memory 1004 may includenon-volatile memory (e.g., flash memory, read-only memory (ROM),programmable read-only memory (PROM), erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM)), volatile memory (e.g., random access memory (RAM), staticrandom access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulkstorage memory (e.g., CD-ROM and/or DVD-ROM, hard drives), and/or thelike. The memory 1004 may include a non-transitory computer-readablestorage medium having program instructions 1010 stored therein. Theprogram instructions 1010 may include program modules 1012 that areexecutable by a computer processor (e.g., the processor 1006) to causethe functional operations described herein, including, for example, oneor more of the methods 300 and/or 350. In the context of a computersystem of the client device 106, the program modules 1012 may includeone or more modules for performing some or all of the operationsdescribed with regard to the client device 106. In the context of acomputer system of the application server 110, the program modules 1012may include a one or more modules for performing some or all of theoperations described with regard to the application server 110. In thecontext of other components of the search system 102 (e.g., data sources112, forwarder 114, indexers 116, and/or the like), the program modules1012 may include a one or more modules for performing some or all of theoperations described with regard to the respective components.

The processor 1006 may be any suitable processor capable ofexecuting/performing program instructions. The processor 1006 mayinclude a central processing unit (CPU) that carries out programinstructions (e.g., the program instructions of the program module(s)1012) to perform the arithmetical, logical, and input/output operationsdescribed herein. The processor 1006 may include one or more processors.The I/O interface 1008 may provide an interface for communication withone or more I/O devices 1014, such as a joystick, a computer mouse, akeyboard, a display screen (e.g., an electronic display for displaying agraphical user interface (GUI)), and/or the like. The I/O devices 1014may include one or more of the user input devices. The I/O devices 1014may be connected to the I/O interface 1008 via a wired or a wirelessconnection. The I/O interface 1008 may provide an interface forcommunication with one or more external devices 1016, such as othercomputers, networks, and/or the like. In some embodiments, the I/Ointerface 1008 may include an antenna, transceiver, and/or the like.

1.1 Overview of Example Performance Data System

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” wherein each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” wherein time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, whereinspecific data items with specific data formats reside at predefinedlocations in the data. For example, structured data can include dataitems stored in fields in a database table. In contrast, unstructureddata does not have a predefined format. This means that unstructureddata can comprise various data items having different data types thatcan reside at different locations. For example, when the data source isan operating system log, an event can include one or more lines from theoperating system log containing raw data that can include differenttypes of performance and diagnostic information associated with aspecific point in time. Examples of data sources from which an event maybe derived include, but are not limited to: web servers; applicationservers; databases; firewalls; routers; operating systems; and softwareapplications that execute on computer systems, mobile devices, andsensors. The data generated by such data sources can be produced invarious forms including, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, and sensor measurements. An eventtypically includes a timestamp that may be derived from the raw data inthe event, or may be determined through interpolation between temporallyproximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, wherein theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly,” when it isneeded (e.g., at search time), rather than at ingestion time of the dataas in traditional database systems. Because the schema is not applied toevent data until it is needed (e.g., at search time), it is referred toas a “late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the time-stamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction rulecomprises a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about theevents rather than data in the events themselves can be createdautomatically. For example, such default fields can specify: atime-stamp for the event data; a host from which the event dataoriginated; a source of the event data; and a source type for the eventdata. These default fields may be determined automatically when theevents are created, indexed or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

1.2 Data Server System

FIG. 5 presents a block diagram of an exemplary event-processing system1100, similar to the SPLUNK® ENTERPRISE system. System 1100 includes oneor more forwarders 1101 that collect data obtained from a variety ofdifferent data sources 1105, and one or more indexers 1102 that store,process, and/or perform operations on this data, wherein each indexeroperates on data contained in a specific data store 1103. Theseforwarders and indexers can comprise separate computer systems in a datacenter, or may alternatively comprise separate processes executing onvarious computer systems in a data center.

During operation, the forwarders 1101 identify which indexers 1102 willreceive the collected data and then forward the data to the identifiedindexers. Forwarders 1101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders 1101next determine which indexers 1102 will receive each data item and thenforward the data items to the determined indexers 1102.

Note that distributing data across different indexers facilitatesparallel processing. This parallel processing can take place at dataingestion time, because multiple indexers can process the incoming datain parallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

System 1100 and the processes described below with respect to FIGS. 5-9are further described in “Exploring Splunk Search Processing Language(SPL) Primer and Cookbook” by David Carasso, CITO Research, 2012, and in“Optimizing Data Analysis With a Semi-Structured Time Series Database”by Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, and Steve Zhang,SLAML, 2010, each of which is hereby incorporated herein by reference inits entirety for all purposes.

1.3 Data Ingestion

FIG. 6 presents a flowchart illustrating how an indexer processes,indexes, and stores data received from forwarders in accordance with thedisclosed embodiments. At block 1201, the indexer receives the data fromthe forwarder. Next, at block 1202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks, and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, wherein the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, wherein the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces, orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 1203.As mentioned above, these timestamps can be determined by extracting thetime directly from the data in the event, or by interpolating the timebased on timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 1204, for example, by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 1205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in events in block 1206. Then, atblock 1207 the indexer includes the identified keywords in an index,which associates each stored keyword with references to eventscontaining that keyword (or to locations within events where thatkeyword is located). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign or acolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2.”

Finally, the indexer stores the events in a data store at block 1208,wherein a timestamp can be stored with each event to facilitatesearching for events based on a time range. In some cases, the storedevents are organized into a plurality of buckets, wherein each bucketstores events associated with a specific time range. This not onlyimproves time-based searches, but it also allows events with recenttimestamps that may have a higher likelihood of being accessed to bestored in faster memory to facilitate faster retrieval. For example, abucket containing the most recent events can be stored as flash memoryinstead of on hard disk.

Each indexer 1102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 1103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example, using map-reducetechniques, wherein each indexer returns partial responses for a subsetof events to a search head that combines the results to produce ananswer for the query. By storing events in buckets for specific timeranges, an indexer may further optimize searching by looking only inbuckets for time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as is described in. U.S. patent application Ser. No. 14/266,812filed on Apr. 30 2014, and in U.S. application patent Ser. No.14/266,817 also filed on Apr. 30, 2014, which are hereby incorporated byreference.

1.4 Query Processing

FIG. 7 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient at block 1301. Next, at block 1302, the search head analyzes thesearch query to determine what portions can be delegated to indexers andwhat portions need to be executed locally by the search head. At block1303, the search head distributes the determined portions of the queryto the indexers. Note that commands that operate on single events can betrivially delegated to the indexers, while commands that involve eventsfrom multiple indexers are harder to delegate.

Then, at block 1304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 1304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 1305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending upon what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by the system 1100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these settings to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

1.5 Field Extraction

FIG. 8 presents a block diagram illustrating how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 1402 is received at a queryprocessor 1404. Query processor 1404 includes various mechanisms forprocessing a query, wherein these mechanisms can reside in a search head1104 and/or an indexer 1102. Note that the exemplary search query 1402illustrated in FIG. 8 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. SPL isa pipelined search language in which a set of inputs is operated on by afirst command in a command line, and then a subsequent command followingthe pipe symbol “|” operates on the results produced by the firstcommand, and so on for additional commands. Search query 1402 can alsobe expressed in other query languages, such as the Structured QueryLanguage (SQL) or any suitable query language.

Upon receiving search query 1402, query processor 1404 sees that searchquery 1402 includes two fields “IP” and “target.” Query processor 1404also determines that the values for the “IP” and “target” fields havenot already been extracted from events in data store 1414, andconsequently determines that query processor 1404 needs to useextraction rules to extract values for the fields. Hence, queryprocessor 1404 performs a lookup for the extraction rules in a rule base1406, wherein the rule base 1406 maps field names to correspondingextraction rules and obtains extraction rules 1408-1409, whereinextraction rule 1408 specifies how to extract a value for the “IP” fieldfrom an event, and extraction rule 1409 specifies how to extract a valuefor the “target” field from an event. As is illustrated in FIG. 8,extraction rules 1408-1409 can comprise regular expressions that specifyhow to extract values for the relevant fields. Suchregular-expression-based extraction rules are also referred to as “regexrules.” In addition to specifying how to extract field values, theextraction rules may also include instructions for deriving a fieldvalue by performing a function on a character string or a valueretrieved by the extraction rule. For example, a transformation rule maytruncate a character string, or convert the character string into adifferent data format. In some cases, the query itself can specify oneor more extraction rules.

Next, query processor 1404 sends extraction rules 1408-1409 to a fieldextractor 1412, which applies extraction rules 1408-1409 to events1416-1418 in a data store 1414. Note that data store 1414 can includeone or more data stores, and extraction rules 1408-1409 can be appliedto large numbers of events in data store 1414, and are not meant to belimited to the three events 1416-1418 illustrated in FIG. 8. Moreover,the query processor 1404 can instruct field extractor 1412 to apply theextraction rules to all of the events in a data store 1414, or to asubset of the events that have been filtered based on some criteria.

Next field extractor 1412 applies extraction rule 1408 for the firstcommand “Search IP=“10*” to events in data store 1414 including events1416-1418. Extraction rule 1408 is used to extract values for the IPaddress field from events in data store 1414 by looking for a pattern ofone or more digits, followed by a period, followed again by one or moredigits, followed by another period, followed again by one or moredigits, followed by another period, and followed again by one or moredigits. Next, field extractor 1412 returns field values 1420 to queryprocessor 1404, which uses the criterion IP=“10*” to look for IPaddresses that start with “10”. Note that events 1416 and 1417 matchthis criterion, but event 1418 does not, so the result set for the firstcommand is events 1416-1417.

Query processor 1404 then sends events 1416-1417 to the next command“stats count target.” To process this command, query processor 1404causes field extractor 1412 to apply extraction rule 1409 to events1416-1417. Extraction rule 1409 is used to extract values for the targetfield for events 1416-1417 by skipping the first four commas in events1416-1417, and then extracting all of the following characters until acomma or period is reached. Next, field extractor 1412 returns fieldvalues 1421 to query processor 1404, which executes the command “statscount target” to count the number of unique values contained in thetarget fields, which in this example produces the value “2” that isreturned as a final result 1422 for the query.

Note that the query results can be returned to a client, a search head,or any other system component for further processing. In general, queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or a chart, generated from the values.

1.6 Exemplary Search Screen

FIG. 10A illustrates an exemplary search screen 1600 in accordance withthe disclosed embodiments. Search screen 1600 includes a search bar 1602that accepts user input in the form of a search string. It also includesa time range picker 1612 that enables the user to specify a time rangefor the search. For “historical searches,” the user can select aspecific time range, or alternatively a relative time range, such as“today,” “yesterday,” or “last week.” For “real-time searches,” the usercan select the size of a preceding time window to search for real-timeevents. Search screen 1600 also initially displays a “data summary”dialog as is illustrated in FIG. 10B that enables the user to selectdifferent sources for the event data, for example, by selecting specifichosts and log files.

After the search is executed, the search screen 1600 can display theresults through search results tabs 1604, wherein search results tabs1604 include: an “events tab” that displays various information aboutevents returned by the search; a “statistics tab” that displaysstatistics about the search results; and a “visualization tab” thatdisplays various visualizations of the search results. The events tabillustrated in FIG. 10A displays a timeline graph 1605 that graphicallyillustrates the number of events that occurred in one-hour intervalsover the selected time range. It also displays an events list 1608 thatenables a user to view the raw data in each of the returned events. Itadditionally displays a fields sidebar 1606 that includes statisticsabout occurrences of specific fields in the returned events, including“selected fields” that are pre-selected by the user, and “interestingfields” that are automatically selected by the system based onpre-specified criteria.

1.7 Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

1.7.1 Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 9 illustrates how a searchquery 1501 received from a client at search head 1104 can split into twophases, including: (1) a “map phase” comprising subtasks 1502 (e.g.,data retrieval or simple filtering) that may be performed in paralleland are “mapped” to indexers 1102 for execution, and (2) a “reducephase” comprising a merging operation 1503 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 1501, search head 1104modifies search query 1501 by substituting “stats” with “prestats” toproduce search query 1502, and then distributes search query 1502 to oneor more distributed indexers, which are also referred to as “searchpeers”. Note that search queues may generally specify search criteria oroperations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 5, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 1503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

1.7.2 Keyword Index

As described above with reference to the flowcharts in FIGS. 6 and 7,the event-processing system 1100 can construct and maintain one or morekeyword indices to facilitate rapidly identifying events containingspecific keywords. This can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

1.7.3 High Performance Analytics Store

To speed up certain types of queries, some embodiments of the system1100 make use of a high performance analytics store, which is referredto as a “summarization table,” that contains entries for specificfield-value pairs. Each of these entries keeps track of instances of aspecific value in a specific field in the event data and includesreferences to events containing the specific value in the specificfield. For example, an exemplary entry in a summarization table can keeptrack of occurrences of the value “94107” in a “ZIP code” field of a setof events, wherein the entry includes references to all of the eventsthat contain the value “94107” in the ZIP code field. This enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field, because the system canexamine the entry in the summarization table to count instances of thespecific value in the field without having to go through the individualevents or do extractions at search time. Also, if the system needs toprocess all events that have a specific field-value combination, thesystem can use the references in the summarization table entry todirectly access the events to extract further information without havingto search all of the events to find the specific field-value combinationat search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor & specific time range, wherein a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, wherein theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover all of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014, which ishereby incorporated by reference.

1.7.4 Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether the generation of updated reports can be accelerated by creatingintermediate summaries. (This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example, where a value in the report depends onrelationships between events from different time periods.) If reportscan be accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only events within thetime period that meet the specified criteria. Similarly, if the queryseeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that matches thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so only the newer eventdata needs to be processed while generating an updated report. Thesereport acceleration techniques are described in more detail in U.S. Pat.No. 8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696,issued on Apr. 2, 2011, which are hereby incorporated by reference.

1.8 Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that make it easy for developers to createapplications to provide additional capabilities. One such application isthe SPLUNK® APP FOR ENTERPRISE SECURITY, which performs monitoring andalerting operations and includes analytics to facilitate identifyingboth known and unknown security threats based on large volumes of datastored by the SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, wherein the extracteddata is typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volumes, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. patentapplication Ser. Nos. 13/956,252, and 13/956,262, which are herebyincorporated by reference. Security-related information can also includeendpoint information, such as malware infection data and systemconfiguration information, as well as access control information, suchas login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 11A illustratesan exemplary key indicators view 1700 that comprises a dashboard, whichcan display a value 1701, for various security-related metrics, such asmalware infections 1702. It can also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338filed Jul. 31,2013, which is hereby incorporated by reference.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 11B illustrates an exemplary incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in one-hour time intervals overthe selected time range. It additionally displays an events list 1714that enables a user to view a list of all of the notable events thatmatch the criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further in“http://docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

1.9 Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example, byextracting pre-specified data items from the performance data andstoring them in a database to facilitate subsequent retrieval andanalysis at search time. However, the rest of the performance data isnot saved and is essentially discarded during pre-processing. Incontrast, the SPLUNK® APP FOR VMWARE® stores large volumes of minimallyprocessed performance information and log data at ingestion time forlater retrieval and analysis at search time when a live performanceissue is being investigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. Pat. No.14/167,316 filed Jan. 29, 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00,http://pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Exemplary node-expansion operations are illustrated in FIG.11C, wherein nodes 1733 and 1734 are selectively expanded. Note thatnodes 1731-1739 can be displayed using different patterns or colors torepresent different performance states, such as a critical state, awarning state, a normal state, or an unknown/offline state. The ease ofnavigation provided by selective expansion in combination with theassociated performance-state information enables a user to quicklydiagnose the root cause of a performance problem. The proactivemonitoring tree is described in further detail in U.S. patentapplication Ser. No. 14/235,490 filed on Apr. 15, 2014, which is herebyincorporated by reference.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data and associated performance metrics, for theselected time range. For example, the screen illustrated in FIG. 11Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 1742 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data quickly determine the rootcause of a performance problem. This user interface is described in moredetail in U.S. patent application Ser. No. 14/167,316 filed on Jan. 29,2014, which is hereby incorporated by reference.

Further modifications and embodiments of various aspects of thedisclosure will be apparent to those skilled in the art in view of thisdescription. Accordingly, this description is to be construed asillustrative only and is for the purpose of teaching those skilled inthe art the general manner of carrying out the disclosure. It is to beunderstood that the forms of the disclosure shown and described hereinare to be taken as examples of embodiments. Elements and materials maybe substituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of thedisclosure may be utilized independently, all as would be apparent toone skilled in the art after having the benefit of this description ofthe disclosure. Changes may be made in the elements described hereinwithout departing from the spirit and scope of the disclosure asdescribed in the following claims. Headings used herein are fororganizational purposes only and are not meant to be used to limit thescope of the description.

It will be appreciated that the methods described are exampleembodiments of methods that may be employed in accordance with thetechniques described herein. The methods may be modified to facilitatevariations of their implementation and use. The order of the methods andthe operations provided therein may be changed, and various elements maybe added, reordered, combined, omitted, modified, etc. Portions of themethods may be implemented in software, hardware, or a combinationthereof. Some or all of the portions of the methods may be implementedby one or more of the processors/modules/applications described herein.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include,”“including,” and “includes” mean including, but not limited to. As usedthroughout this application, the singular forms “a”, “an,” and “the”include plural referents unless the content clearly indicates otherwise.Thus, for example, reference to “an element” may include a combinationof two or more elements. As used throughout this application, the phrase“based on” does not limit the associated operation to being solely basedon a particular item. Thus, for example, processing “based on” data Amay include processing based at least in part on data A and based atleast in part on data B unless the content clearly indicates otherwise.Unless specifically stated otherwise, as apparent from the discussion,it is appreciated that throughout this specification discussionsutilizing terms such as “processing,” “computing,” “calculating,”“determining,” or the like refer to actions or processes of a specificapparatus, such as a special purpose computer or a similar specialpurpose electronic processing/computing device. In the context of thisspecification, a special purpose computer or a similar special purposeelectronic processing/computing device is capable of manipulating ortransforming signals, typically represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of the specialpurpose computer or similar special purpose electronicprocessing/computing device.

1-30. (canceled)
 31. A method of executing a scheduled summary request, said method comprising: receiving raw machine-generated data; segmenting the raw machine-generated data into a set of time-stamped event records; indexing and storing the set of time-stamped event records in two or more partitions of event records; scheduling a summary request for the two or more partitions, wherein said summary request is scheduled based on a processing load requirement; sending the summary request to a plurality of indexers when the processing load requirement has been met; using the indexers, identifying a bucket for serving the summary request; locking a summary directory associated with the bucket; writing a summary for the bucket to the summary directory; and unlocking the summary directory.
 32. The method as described in claim 31, wherein the two or more partitions are stored in a memory of an indexer.
 33. The method as described in claim 32, further comprising receiving the summary request at the indexer from one or more search heads.
 34. The method as described in claim 31, wherein each of the two or more partitions is associated with a timespan and comprises time-stamped event records having a respective timestamp corresponding to a respective time in the timespan.
 35. The method as described in claim 31, wherein said processing load requirement comprises a number of processes being executed, and further comprising determining that less than the number of processes are currently being executed, and wherein the summary request is generated responsive to the determining that less than the given number of processes are currently being executed.
 36. The method of claim 31, wherein said processing load requirement comprises a processing delay requirement, and further comprising determining that at least an amount of time corresponding to the processing delay requirement has passed since initiating execution of a prior summary request, wherein the summary request is generated responsive to the determining that at least the amount of time has passed since initiating execution of the prior summary request.
 37. The method of claim 31, further comprising: receiving, from an entity, a request for summary data; and providing, to the entity, the summary for the bucket responsive to the request, wherein the entity is configured to generate a result based at least in part on the contents of the summary for the bucket.
 38. The method of claim 31, wherein the summary comprises a first subset of values of fields of events corresponding to a data model, and further comprising: receiving, from an entity, a request for summary data; and providing, to the entity, the summary for the bucket responsive to the request, wherein the entity is configured to generate a set of values fields of events for the data model determined based at least in part on the first subset of values of fields.
 39. The method of claim 38, wherein the summary request is scheduled responsive to enabling acceleration of the data model.
 40. The method of claim 31, wherein the summary request is scheduled responsive to enabling acceleration of a report.
 41. The method of claim 31, further comprising: receiving a search request; generating search results based at least in part on the summary for the bucket; and providing the search results in response to the search request.
 42. The method as described in claim 31, further comprising determining that a second summary directory is locked to inhibit a process from writing summary data to the second summary directory.
 43. The method as described in claim 31, further comprising determining that a second summary directory comprises an indication that an entirety of contents of a second partition is summarized
 44. The method as described in claim 31, further comprising determining that the summary directory comprises an indication that less than an entirety of contents of a first partition is summarized.
 45. A system comprising: one or more processors; and one or more memories comprising program instructions stored thereon that are executable by the one or more processors to cause the system to perform a method comprising: receiving raw machine-generated data; segmenting the raw machine-generated data into a set of time-stamped event records; indexing and storing the set of time-stamped event records in two or more partitions of event records; scheduling a summary request for the two or more partitions, wherein said summary request is scheduled based on a processing load requirement; sending the summary request to a plurality of indexers when the processing load requirement has been met; using the indexers, identifying a bucket for serving the summary request; locking a summary directory associated with the bucket; writing a summary for the bucket to the summary directory; and unlocking the summary directory.
 46. The system as described in claim 45, wherein the two or more partitions are stored in a memory of an indexer.
 47. The system as described in claim 46, wherein the method further comprises receiving the summary request at the indexer from one or more search heads.
 48. The system as described in claim 45, wherein each of the two or more partitions is associated with a timespan and comprises time-stamped event records having a respective timestamp corresponding to a respective time in the timespan.
 49. The system as described in claim 45, wherein said processing load requirement comprises a number of processes being executed, and wherein the method further comprises determining that less than the number of processes are currently being executed, and wherein the summary request is generated responsive to the determining that less than the given number of processes are currently being executed.
 50. The system of claim 45, wherein said processing load requirement comprises a processing delay requirement, and wherein the method further comprises determining that at least an amount of time corresponding to the processing delay requirement has passed since initiating execution of a prior summary request, wherein the summary request is generated responsive to the determining that at least the amount of time has passed since initiating execution of the prior summary request.
 51. The system of claim 45, wherein the method further comprises: receiving, from an entity, a request for summary data; and providing, to the entity, the summary for the bucket responsive to the request, wherein the entity is configured to generate a result based at least in part on the contents of the summary for the bucket.
 52. The system of claim 45, wherein the summary comprises a first subset of values of fields of events corresponding to a data model, and wherein the method further comprises: receiving, from an entity, a request for summary data; and providing, to the entity, the summary for the bucket responsive to the request, wherein the entity is configured to generate a set of values fields of events for the data model determined based at least in part on the first subset of values of fields.
 53. The system of claim 52, wherein the summary request is scheduled responsive to enabling acceleration of the data model.
 54. The system of claim 45, wherein the summary request is scheduled responsive to enabling acceleration of a report.
 55. The system of claim 45, wherein the method further comprises: receiving a search request; generating search results based at least in part on the summary for the bucket; and providing the search results in response to the search request.
 56. The system as described in claim 45, wherein the method further comprises determining that a second summary directory is locked to inhibit a process from writing summary data to the second summary directory.
 57. The system as described in claim 45, wherein the method further comprises determining that a second summary directory comprises an indication that an entirety of contents of a second partition is summarized.
 58. The system as described in claim 45, wherein the method further comprises determining that the summary directory comprises an indication that less than an entirety of contents of a first partition is summarized.
 59. One or more non-transitory computer-readable medium comprising program instructions stored thereon that are executable by one or more processors to implement a method comprising: receiving raw machine-generated data; segmenting the raw machine-generated data into a set of time-stamped event records; indexing and storing the set of time-stamped event records in two or more partitions of event records; scheduling a summary request for the two or more partitions, wherein said summary request is scheduled based on a processing load requirement; sending the summary request to a plurality of indexers when the processing load requirement has been met; using the indexers, identifying a bucket for serving the summary request; locking a summary directory associated with the bucket; writing a summary for the bucket to the summary directory; and unlocking the summary directory. 